{"id":"W2913199598","doi":"10.1007/s11760-019-01435-2","title":"Automatic visual inspection of thermoelectric metal pipes","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Preprocessor; Artificial intelligence; Computer vision; Computer science; Cartesian coordinate system; Image processing; Classifier (UML); Thermoelectric effect; Pattern recognition (psychology); Image (mathematics); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002275156,0.0001057625,0.0001938411,0.0001472518,0.00006245871,0.00006563675,0.00003221688,0.00007382998,0.00008047678],"category_scores_gemma":[0.00001337516,0.00009002096,0.0000412483,0.0002890243,0.00001864789,0.0003736728,0.000009353809,0.0001191347,0.00002219795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002202504,"about_ca_system_score_gemma":0.00001877169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002306537,"about_ca_topic_score_gemma":8.390254e-7,"domain_scores_codex":[0.9993513,0.00002924501,0.0002311773,0.0001194916,0.0001355493,0.0001332874],"domain_scores_gemma":[0.9997666,0.00003408506,0.00006373971,0.00005167953,0.00005354575,0.00003031983],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000133034,0.00001149666,0.0001576832,0.000303336,0.00001930423,0.000001283279,0.0002215217,0.0001266939,0.6467963,0.00000540307,0.00003537817,0.3523082],"study_design_scores_gemma":[0.0005920897,0.0002940492,0.001165376,0.0002848191,0.00004297859,0.00004074394,0.000327007,0.5603406,0.436412,0.0001246847,0.0001463253,0.0002293924],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824221,0.001086507,0.01348389,0.000003019583,0.0001393166,0.0001509751,8.103768e-7,0.0002518192,0.002461623],"genre_scores_gemma":[0.9997056,0.00001098157,0.000102592,0.000006293187,0.0001036466,0.000003509581,9.293302e-7,0.00001905268,0.00004741918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5602139,"threshold_uncertainty_score":0.3670948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007594227519139477,"score_gpt":0.2354588806026671,"score_spread":0.2278646530835277,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}